C-TREND Temporal Cluster Graphs for Identifying and Visualizing

Document Sample
C-TREND Temporal Cluster Graphs for Identifying and Visualizing Powered By Docstoc
					C-TREND: Temporal Cluster Graphs
                 for
Identifying and Visualizing Trends in
  Multiattribute Transactional Data
                       ABSTRACT
   Organizations and firms are capturing increasingly more data about
    their customers, suppliers, competitors, and business environment.
    Most of this data is multiattribute (multidimensional) and temporal
    in nature. Data mining and business intelligence techniques are
    often used to discover patterns in such data; however, mining
    temporal relationships typically is a complex task. This paper
    propose a new data analysis and visualization technique for
    representing trends in multiattribute temporal data using a
    clustering based approach. This paper introduce Cluster-based
    Temporal Representation of EvenT Data (C-TREND), a system
    that implements the temporal cluster graph construct, which maps
    multiattribute temporal data to a two-dimensional directed graph
    that identifies trends in dominant data types over time.
   This paper present temporal clustering-based
    technique, discuss its algorithmic implementation and
    performance, demonstrate applications of the
    technique by analyzing data on wireless networking
    technologies and baseball batting statistics, and
    introduce a set of metrics for further analysis of
    discovered trends.
          EXISTING SYSTEM
   Existing algorithms uses matrices to produce
    partition.
   Distance between the matrices is used for
    calculation.

Disadvantage:
 Existing Schemes Consumes more time.
       PROPOSED SYSTEM
 In our project we use DENDROGRAM Data
  structure for storing and Extracting cluster
  solutions generated by hierarchical clustering
  algorithms
 Calculations are made using Tree structure

  Advantages:
 Efficiency is considerably increased.

 N is user defined.
                 MODULES
   Transformation of data’s from excel sheet
   Creation of dataset
   Partition and clustering
   Denogram sorting
   Display the time of sorting.
   Extracting values according to N
     SYSTEM REQUIREMENT
        SPECIFICATION
Hardware Requirements:
 Processor        : Pentium IV
 Hard Disk        : 80 GB.
 RAM              : 512 MB.
Software Requirements:
 Operating system : Windows XP
 Technology       : Java 1.6
 Web-Server       :Tomcat 5.5
 Data base        :SQL Server 2000
Thank You!...

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:13
posted:2/10/2012
language:
pages:8